Set up an embedded visualization solution in under 5 minutes. This short demo highlights the steps needed to build a highly customized application.

Danggeun market is an online marketplace in Korea focussed on used goods sales in the neighbourhood. This session will detail out how Daangn uses druid to speed up clickstream data analytics. Realtime data is ingested into druid from kafka. The ingested data is queried in druid using the ability to query a nested column in druid. The session will wrap-up looking at some performance numbers and dashboards and discussing future direction including merging topics using kstream.

Case study to share my experience how easy is to setup a working Analytics DB by using Imply Polaris. Leverage the batch ingestion APIs using python, using Apache airflow scheduler. How we leveraging Theta sketches for unique property listing calculation. Overall experience with Polaris while I've built this PoC to fulfill our Self Serve Analytics needs.

At Netflix, our engineers often need to see metrics as distributions to get a full picture of how our users are experiencing playback. For example the “play delay”, or the time taken from hitting play to seeing the video start. Measuring this as an average would lose a lot of detail. We have tried various data types to store these distributions in a way that we can query across a massive dataset and get results in low seconds to remain interactive.

Come learn about the way that TrueCar uses Druid as the engine to optimize the buying experience through clickstream analytics. Anil will introduce us to the role that Druid played in modernizing TrueCar's data pipeline and migrating from batch to streaming analytics. This has enabled them to unlock faster decision-making through real-time dashboards and anomaly detection across digital interaction data.

How do developers at Netflix, Confluent, and Reddit do more with analytics? They joined a community of like-minded people who bring analytics to applications to power a new generation of operational workflows. We’ll dive into the use cases and applications built with Apache Druid.

SQL is a powerful and elegant query language for databases. Druid is increasingly supporting and moving forward with SQL, which is arguably easier to learn and write than the original native (JSON) query language. This talk will discuss the Druid implementation of SQL, focusing on querying data. How does it work? What parts of SQL are and aren't supported? What are some gotchas, and tips and tricks, for people new to it? We'll focus on querying data. We'll also briefly discuss the new multi-stage query engine and SQL syntax for ingesting or transforming data, but the main focus will be on writing good, efficient queries.

VMware NSX Intelligence is a distributed analytics platform powered by Druid that can process and visualize network traffic flows, detect anomalous network behaviors, and recommend security policies. We will introduce our data pipeline (Kafka, Spark, Druid), how we ingest and store data with Druid, how we perform different kind of queries to achieve our use cases, and our learnings in the past 3 years.

Apache Druid can handle streaming ingestion jobs at any scale. This occasionally poses challenges. Apache Kafka is a popular distributed event streaming platform, so it’s a natural choice for this presentation. As a community, we hope to improve our existing documentation of Kafka errors, so we’re hoping that this presentation will get the ball rolling. We’ll cover basics about setting up a Kafka streaming ingestion job, including a best practice tip or two such as monitoring your deployment with metrics and logs. We’ll also talk about the location of Kafka related logs within your deployment. We’ll conclude with some common Kafka ingestion errors and solutions, specifically lag and parsing.